Super Data Science: ML & AI Podcast with Jon Krohn 303: Proper Hypothesis Testing For Every Field
Oct 9, 2019
Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
Introduction
00:00 • 5min
Sam Hinton on the Super Day Science Podcast
05:07 • 3min
The 2011 Noble Prize in Physics
07:39 • 2min
The Evolution of Dark Energy
09:42 • 3min
The Heisenberg on Predictability and General Relativity
12:53 • 2min
How to Be an Australian Survivor
14:29 • 3min
The Challenges of Being an Academic
17:26 • 2min
Python for Statistical Analysis Course Launched on Udemy and on SDS
19:26 • 3min
The Importance of Graphic Exploration in Statistics
22:00 • 3min
Python vs. R: The Future of Astrophysics
24:32 • 2min
Python vs R: Which Is Better for You?
26:22 • 2min
Python and the Future of Data Science
28:26 • 4min
The Null Hypothesis for Election Interference
32:15 • 4min
The Null Hypothesis and the H1 Hypothesis
35:57 • 4min
The Importance of P-Values in Astrophysics
39:31 • 2min
Why You Should Care About Statistical Significance in Data Science
41:44 • 3min
The Importance of Thinking in Probabilities
44:30 • 4min
The Importance of Multiple Methods in Astrophysics
48:39 • 2min
The Difference Between Frequentist and Bayesian Statistics
50:38 • 2min
The Differences Between Bayesian Statistics and Frequent Statistics
53:02 • 4min
The Advantages of Bayesian Statistics Over Frequent Statistics
56:33 • 2min
How to Learn Bayesian Statistics
59:00 • 3min
What Is Your Dream Position?
01:02:25 • 2min
Bayesian Methods in Cosmology
01:04:52 • 2min
The Power of Bayesian Inference
01:06:56 • 3min